Measurement Calibration Method and Device for SOC Based on OCV-SOC Estimation

Abstract

This application relates to the technical field of battery application and management, and particularly relates to a method and device for measuring and correcting SOC based on OCV-SOC estimation. The method includes: extracting the measured data of HPPC of a lithium-ion battery; estimating the steady-state OCV of the lithium-ion battery based on the measured data; obtaining the measured OCV-SOC curve of the battery at different temperatures; establishing an OCV-SOC mapping relationship according to the measured OCV-SOC curve of the battery; and correcting the SOC estimated value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship. Thus, the problems in the related art that the OCV cannot be measured in real time, the time required to reach the stable state during OCV measurement is relatively long, and the actual usage requirements cannot be met are solved.

Description

Technical Field

[0001] This application relates to the technical field of battery application and management, and particularly relates to a method and device for measuring and correcting SOC based on OCV-SOC estimation. Background Art

[0002] Since the emergence of lithium-ion batteries, due to advantages such as high energy density, high power density, low self-discharge rate, and stability, they have been widely used and developed rapidly. For the design of battery application and management systems, it is very necessary to know the SOC (State of Charge) of the battery in real time to ensure its safety, reliability, and stable operation.

[0003] In related technologies, the estimation algorithms of SOC can be mainly divided into: open circuit voltage method, ampere-hour integration method, electrochemical model method, machine learning method, etc. Among them, the open circuit voltage method mainly determines SOC by establishing a mapping relationship between SOC and the battery's OCV (Open Circuit Voltage) and looking up the table during operation; the ampere-hour integration method calculates the current SOC value by integrating the current under the condition of knowing the maximum available capacity and the initial SOC of the battery; the machine learning method needs to rely on a large amount of test data to establish a mapping function between the external characteristics of the battery and SOC.

[0004] However, in related technologies, the OCV cannot be measured in real time, and the time required to reach the stable state during OCV measurement is relatively long, which cannot meet the actual usage requirements and urgently needs improvement. Summary of the Invention

[0005] This application provides a method and device for measuring and correcting SOC based on OCV-SOC estimation to solve the problems in related technologies that the OCV cannot be measured in real time, the time required to reach the stable state during OCV measurement is relatively long, and it cannot meet the actual usage requirements.

[0006] The first aspect embodiment of this application provides a method for measuring and correcting SOC based on OCV-SOC estimation, including the following steps: extracting the measured data of the HPPC (Hybrid Pulse Power Characteristic) of the lithium-ion battery; estimating the steady-state OCV of the lithium-ion battery based on the measured data; obtaining the measured curves of battery OCV-SOC at different temperatures; establishing an OCV-SOC mapping relationship according to the measured curves of battery OCV-SOC; and correcting the SOC estimated value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship.

[0007] Optionally, in an embodiment of the present application, estimating the steady-state OCV of the lithium-ion battery includes: obtaining a charging envelope and a discharging envelope according to the measured data; and obtaining the steady-state OCV according to the average value of the charging envelope and the discharging envelope.

[0008] Optionally, in an embodiment of the present application, estimating the steady-state OCV of the lithium-ion battery includes: based on the measured data, performing an optimal fit on the data within a preset gap of stopped discharging with an exponential curve to obtain a steady-state extrapolated value; and based on the steady-state extrapolated value, determining the steady-state OCV with the average value of the upper limit of the OCV region during charging and the lower limit of the OCV region during discharging.

[0009] Optionally, in an embodiment of the present application, establishing an OCV-SOC mapping relationship according to the measured battery OCV-SOC curve includes: performing a piecewise fit on the measured battery OCV-SOC curve to obtain the OCV-SOC mapping relationship.

[0010] Optionally, in an embodiment of the present application, establishing an OCV-SOC mapping relationship according to the measured battery OCV-SOC curve includes: based on the measured battery OCV-SOC curve, using a preset gradient boosting tree to obtain the OCV-SOC mapping relationship.

[0011] An embodiment of the second aspect of the present application provides a measurement correction device for SOC based on OCV-SOC estimation, including: an extraction module for extracting the measured data of the HPPC of the lithium-ion battery; an estimation module for estimating the steady-state OCV of the lithium-ion battery based on the measured data; an acquisition module for obtaining the measured battery OCV-SOC curve at different temperatures; a generation module for establishing an OCV-SOC mapping relationship according to the measured battery OCV-SOC curve; and a correction module for correcting the SOC estimated value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship.

[0012] Optionally, in an embodiment of the present application, the estimation module includes: a first generation unit for obtaining a charging envelope and a discharging envelope according to the measured data; and a second generation unit for obtaining the steady-state OCV according to the average value of the charging envelope and the discharging envelope.

[0013] Optionally, in an embodiment of the present application, the estimation module includes: a fitting unit for performing an optimal fit on the data within a preset gap of stopped discharging with an exponential curve based on the measured data to obtain a steady-state extrapolated value; and a determination unit for determining the steady-state OCV based on the steady-state extrapolated value with the average value of the upper limit of the OCV region during charging and the lower limit of the OCV region during discharging.

[0014] Optionally, in an embodiment of the present application, the generation module includes: a third generation unit configured to perform piecewise fitting on the measured OCV-SOC curve of the battery to obtain the OCV-SOC mapping relationship.

[0015] Optionally, in an embodiment of the present application, the generation module includes: an acquisition unit configured to use a preset gradient boosting tree to obtain the OCV-SOC mapping relationship based on the measured OCV-SOC curve of the battery.

[0016] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the method for measuring and correcting SOC based on OCV-SOC estimation as described in the above embodiments.

[0017] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, and when the program is executed by a processor, it implements the method for measuring and correcting SOC based on OCV-SOC estimation as described above.

[0018] An embodiment of the fifth aspect of the present application provides a computer program product including a computer program, and when the computer program is executed by a processor, it implements the method for measuring and correcting SOC based on OCV-SOC estimation as described above.

[0019] Embodiments of the present application can estimate the steady-state OCV of a lithium-ion battery and the mapping relationship between the battery OCV-SOC at different temperatures according to the measured data of the HPPC of the extracted lithium-ion battery, correct the SOC estimated value under the mapping, thereby improving the accuracy of the OCV-SOC mapping SOC estimated value, being applicable to a variety of usage scenarios, and better meeting the actual usage requirements. Thus, it solves the problems in the related art that the OCV cannot be measured in real time, the time required to reach the stable state during OCV measurement is relatively long, and the actual usage requirements cannot be met.

[0020] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0021] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0022] Figure 1 It is a flowchart of a method for measuring and correcting SOC based on OCV-SOC estimation according to an embodiment of the present application;

[0023] Figure 2 A block diagram showing the way to obtain the steady-state OCV by averaging the envelope line provided in one embodiment of the present application;

[0024] Figure 3 A block diagram showing the way to determine the steady-state OCV by steady-state extrapolation provided in one embodiment of the present application;

[0025] Figure 4 A block diagram showing the measured curves of battery OCV-SOC at different temperatures provided in one embodiment of the present application;

[0026] Figure 5 A block diagram showing a measurement correction device for SOC based on OCV-SOC estimation provided in an embodiment of the present application;

[0027] Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of the present application. Detailed implementation manners

[0028] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0029] The measurement correction method and device for SOC based on OCV-SOC estimation according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problems mentioned in the above background art that the OCV cannot be measured in real time, and the time required to reach the stable state during OCV measurement is relatively long, which cannot meet the actual usage requirements, the present application provides a measurement correction method for SOC based on OCV-SOC estimation. In this method, the steady-state OCV of a lithium-ion battery and the mapping relationship between battery OCV-SOC at different temperatures can be estimated according to the measured data of HPPC of the extracted lithium-ion battery, and the SOC estimated value under the mapping can be corrected, thereby improving the accuracy of the SOC estimated value of the OCV-SOC mapping and being applicable to various usage scenarios, and more meeting the actual usage requirements. Thus, the problems in the related art that the OCV cannot be measured in real time, and the time required to reach the stable state during OCV measurement is relatively long, which cannot meet the actual usage requirements, etc., are solved.

[0030] Specifically, Figure 1 A flowchart of a measurement correction method for SOC based on OCV-SOC estimation provided in an embodiment of the present application.

[0031] As Figure 1As shown in the figure, the method for measuring and correcting the SOC based on OCV-SOC estimation includes the following steps:

[0032] In step S101, the measured data of the HPPC of the lithium-ion battery is extracted.

[0033] It can be understood that the lithium-ion battery can but is not limited to including 18650 lithium batteries, polymer lithium batteries, lithium iron phosphate batteries, etc., and the present application does not make specific limitations. In addition, the embodiments of the present application can also select other types of batteries according to actual usage requirements, and the present application does not make specific limitations.

[0034] As a possible implementation manner, the embodiments of the present application can analyze and extract the measured data of the HPPC of the lithium-ion battery. Among them, the HPPC measured data can reflect the pulse charge and discharge performance of the battery.

[0035] In step S102, based on the measured data, the steady-state OCV of the lithium-ion battery is estimated.

[0036] In the actual execution process, the embodiments of the present application can estimate the OCV of the lithium-ion battery in the steady state through the measured data of the HPPC, and then obtain the steady-state OCV. The estimation method can but is not limited to taking the average of the envelope line, steady-state extrapolation, etc., and the present application does not make specific limitations.

[0037] Optionally, in an embodiment of the present application, estimating the steady-state OCV of the lithium-ion battery includes: obtaining a charge envelope line and a discharge envelope line according to the measured data; obtaining the steady-state OCV according to the average value of the charge envelope line and the discharge envelope line.

[0038] In some embodiments, the embodiments of the present application can obtain a charge envelope line and a discharge envelope line according to the measured data, and then take the average value thereof to obtain the steady-state OCV.

[0039] For example, the embodiments of the present application can take Figure 2 the charging process of the lithium-ion battery shown as an example, and obtain the steady-state OCV by taking the average of the envelope line.

[0040] That is to say, from Figure 2 it can be seen that in the embodiments of the present application, there is a phenomenon of voltage drop at 1 Ah and at every 10 Ah interval thereafter. This is due to the voltage drop of the terminal voltage to the open-circuit voltage caused by the power-off interval during the HPPC measurement process; when discharging, the terminal voltage will rise again. Furthermore, in the embodiments of the present application, the OCV values measured during pauses at different SOCs during the discharge and charge processes can form a dotted envelope. In addition, if the pause time is long enough (for example, 24 hours, and the present application does not make specific limitations), at this time, the dotted lines of the charge and discharge curves of the embodiments of the present application will be the same, and thus the steady-state OCV is reached.

[0041] That is to say, for the two envelope curves obtained from the measured data of charging HPPC and discharging HPPC in the embodiments of the present application: the charging envelope curve and the discharging envelope curve, the area between these two envelope curves can be an OCV estimation area that meets a certain error range. Furthermore, in the embodiments of the present application, the average value of the two envelope curves is set as the steady-state OCV, and thus the steady-state OCV is obtained.

[0042] In some other embodiments, the embodiments of the present application can also obtain the steady-state OCV in other ways of charging envelope curves and discharging envelope curves, such as verbal descriptions, other images, etc., and the present application does not make specific limitations.

[0043] Optionally, in an embodiment of the present application, estimating the steady-state OCV of a lithium-ion battery includes: based on the measured data, performing an optimal fit on the data within a preset interval after stopping discharging with an exponential curve to obtain a steady-state extrapolated value; based on the steady-state extrapolated value, determining the steady-state OCV with the average value of the upper limit of the OCV area during charging and the lower limit of the OCV area during discharging.

[0044] In some embodiments, the embodiments of the present application can perform an optimal fit on the data within a certain interval after stopping discharging with an exponential curve, thereby obtaining a steady-state extrapolated value, and then determining the average value of the upper limit of the OCV area during charging and the lower limit of the OCV area during discharging as the steady-state OCV.

[0045] For example, the embodiments of the present application can Figure 3 take the discharging process of the lithium-ion battery shown as an example and determine the steady-state OCV by means of steady-state extrapolation.

[0046] That is to say, from Figure 3 it can be known that during the discharging process of the lithium-ion battery in the embodiments of the present application, as the SOC decreases, the battery terminal voltage will decrease along the V0 direction. In addition, if the embodiments of the present application stop discharging at t0, the voltage will immediately rise to the level of V1; if discharging is resumed at t2, after a certain interval (such as, 1 minute later, which can also be other values, and the present application does not make specific limitations), the voltage at t3 may have dropped to V3. It can be understood that during the 1-minute interval when the embodiments of the present application stop discharging between t0 and t2, the voltage rises exponentially, and among them, the embodiments of the present application can ignore the short-time constant data between t0 and t1 (5 seconds). Further, the embodiments of the present application can perform an optimal fit on the data within the 1-minute interval after stopping discharging with an exponential curve, thereby obtaining a steady-state extrapolated value V2. Finally, the embodiments of the present application can use the average value of the upper limit of the OCV area during charging and the lower limit of the OCV area during discharging as the rapid extraction value of the steady-state OCV, and thus determine the steady-state OCV.

[0047] In still other embodiments, the steady-state OCV of the present application embodiment can also be obtained in other ways, such as language description, other images, etc., and the present application does not make specific limitations.

[0048] In step S103, the measured OCV-SOC curves of the battery at different temperatures are obtained.

[0049] Those skilled in the art can understand that at different temperatures, the battery's voltage and state of charge will have different performance manifestations, such as changes in battery capacity, internal resistance, etc., and the present application does not make specific limitations.

[0050] As a possible implementation manner, the embodiment of the present application can use related devices to obtain the measured curves of the battery's voltage and state of charge based on different temperature ranges set under different test conditions. For example, the measured OCV-SOC curves of the battery at different temperatures obtained by the embodiment of the present application can be as Figure 4 shown.

[0051] In step S104, an OCV-SOC mapping relationship is established according to the measured OCV-SOC curves of the battery.

[0052] During the actual execution process, the embodiment of the present application can select a suitable method to establish the OCV-SOC mapping relationship according to the obtained measured OCV-SOC curves of the battery. Among them, the method can be but is not limited to methods such as piecewise fitting, preset gradient boosting tree, etc., and can be specifically set by those skilled in the art according to the actual situation, and the present application does not make specific limitations.

[0053] Optionally, in an embodiment of the present application, establishing an OCV-SOC mapping relationship according to the measured OCV-SOC curves of the battery includes: performing piecewise fitting on the measured OCV-SOC curves of the battery to obtain the OCV-SOC mapping relationship.

[0054] It can be understood that the piecewise fitting in the embodiment of the present application can select appropriate break points according to actual usage needs and data characteristics, divide the measured curve into multiple sections, and within each section, use a suitable fitting method (such as linear fitting, polynomial fitting, etc., and the present application does not make specific limitations) to fit the voltage and state of charge data to obtain the fitting curve or equation of each section, and then obtain the OCV-SOC mapping relationship.

[0055] Optionally, in an embodiment of the present application, establishing an OCV-SOC mapping relationship according to the measured OCV-SOC curves of the battery includes: based on the measured OCV-SOC curves of the battery, using a preset gradient boosting tree to obtain the OCV-SOC mapping relationship.

[0056] It can be understood that as a powerful supervised learning algorithm, gradient boosting trees can predict the target variable by constructing a series of decision trees, and then learn and obtain the mapping relationship between OCV and SOC from the measured data of battery OCV-SOC. In addition, in the embodiments of the present application, the performance of gradient boosting trees may be affected by multiple factors, such as the quality of data, the selection of features, the setting of parameters, etc. Therefore, the embodiments of the present application can be tried and adjusted multiple times to obtain the best mapping relationship.

[0057] For example, compared with the linear regression method using the 8th-order coefficient fitting, for the OCV-SOC mapping relationship obtained based on gradient boosting trees, the mean absolute error and the maximum estimation error are reduced by 82.4% and 50.2% respectively.

[0058] In step S105, the SOC estimated value under the mapping is corrected according to the steady-state OCV and the OCV-SOC mapping relationship.

[0059] As a possible implementation, the embodiments of the present application can establish the mapping relationship between OCV and SOC according to the measured curve of battery OCV-SOC, and correct the SOC estimated value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship.

[0060] Next, a specific embodiment is used to introduce in detail the measurement and correction method of SOC based on OCV-SOC estimation proposed in the embodiments of the present application.

[0061] Embodiment 1:

[0062] The embodiments of the present application can make the error between the OCV estimated value and the actual value less than ±10 mV according to the estimated steady-state OCV; the error between the SOC estimated value and the actual value is less than ±1.5% SOC. Compared with the method of determining OCV by estimating the steady-state OCV of a lithium-ion battery, the embodiments of the present application improve the accuracy of the use value of the OCV-SOC mapping relationship through the steady-state OCV, and thus, the estimation of SOC has also been greatly improved.

[0063] According to the measurement and correction method of SOC based on OCV-SOC estimation proposed in the embodiments of the present application, the steady-state OCV of a lithium-ion battery and the mapping relationship of battery OCV-SOC at different temperatures can be estimated according to the measured data of HPPC of the extracted lithium-ion battery, and the SOC estimated value under the mapping can be corrected, thereby improving the accuracy of the SOC estimated value of the OCV-SOC mapping, being applicable to various usage scenarios, and better meeting the actual usage requirements. Thus, the problems in the related art that the OCV cannot be measured in real time, and the time required to reach the stable state during OCV measurement is long and cannot meet the actual usage requirements are solved.

[0064] Next, a measurement correction device for SOC based on OCV-SOC estimation according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0065] Figure 5 It is a block diagram of a measurement correction device for SOC based on OCV-SOC estimation provided according to an embodiment of the present application.

[0066] As Figure 5 shown, the measurement correction device 10 for SOC based on OCV-SOC estimation includes: an extraction module 100, an estimation module 200, an acquisition module 300, a generation module 400, and a correction module 500.

[0067] Among them, the extraction module 100 is used to extract the measured data of the HPPC of the lithium-ion battery.

[0068] The estimation module 200 is used to estimate the steady-state OCV of the lithium-ion battery based on the measured data.

[0069] The acquisition module 300 is used to obtain the measured curves of battery OCV-SOC at different temperatures.

[0070] The generation module 400 is used to establish an OCV-SOC mapping relationship according to the measured curves of battery OCV-SOC.

[0071] The correction module 500 is used to correct the SOC estimated value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship.

[0072] Optionally, in an embodiment of the present application, the estimation module 200 includes: a first generation unit and a second generation unit.

[0073] Among them, the first generation unit is used to obtain a charging envelope and a discharging envelope according to the measured data.

[0074] The second generation unit is used to obtain the steady-state OCV according to the average value of the charging envelope and the discharging envelope.

[0075] Optionally, in an embodiment of the present application, the estimation module 200 includes: a fitting unit and a determination unit.

[0076] Among them, the fitting unit is used to perform the best fit on the data within a preset gap of stopping discharging with an exponential curve based on the measured data to obtain a steady-state extrapolation value.

[0077] The determination unit is used to determine the steady-state OCV based on the steady-state extrapolation value and the average value of the upper limit of the OCV region during charging and the lower limit of the OCV region during discharging.

[0078] Optionally, in an embodiment of the present application, the generation module 400 includes: a third generation unit.

[0079] Wherein, the third generation unit is configured to perform piecewise fitting on the measured curve of the battery OCV-SOC to obtain the OCV-SOC mapping relationship.

[0080] Optionally, in an embodiment of the present application, the generation module 400 includes: an acquisition unit.

[0081] Wherein, the acquisition unit is configured to obtain the OCV-SOC mapping relationship based on the measured curve of the battery OCV-SOC by using a preset gradient boosting tree.

[0082] It should be noted that the foregoing explanation of the embodiments of the measurement correction method for SOC based on OCV-SOC estimation also applies to the measurement correction device for SOC based on OCV-SOC estimation in this embodiment, and will not be elaborated here.

[0083] According to the measurement correction device for SOC based on OCV-SOC estimation proposed in the embodiments of the present application, the steady-state OCV of the lithium-ion battery can be estimated according to the measured data of the HPPC of the extracted lithium-ion battery, and the mapping relationship between the battery OCV-SOC at different temperatures can be obtained, and the SOC estimated value under the mapping can be corrected, thereby improving the accuracy of the SOC estimated value of the OCV-SOC mapping, being applicable to a variety of usage scenarios, and better meeting the actual usage requirements. Thus, the problems in the related art that the OCV cannot be measured in real time, the time required to reach the stable state during OCV measurement is long, and the actual usage requirements cannot be met are solved.

[0084] Figure 6 It is a schematic structural diagram of an electronic device provided according to an embodiment of the present application. The electronic device may include:

[0085] A memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.

[0086] When the processor 602 executes the program, it implements the measurement correction method for SOC based on OCV-SOC estimation provided in the foregoing embodiments.

[0087] Furthermore, the electronic device further includes:

[0088] A communication interface 603 for communication between the memory 601 and the processor 602.

[0089] The memory 601 is used to store a computer program executable on the processor 602.

[0090] The memory 601 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0091] If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0092] Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a single chip, the memory 601, the processor 602, and the communication interface 603 can communicate with each other through an internal interface.

[0093] The processor 602 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0094] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned measurement correction method of SOC based on OCV-SOC estimation is implemented.

[0095] The embodiments of the present application also provide a computer program product, including a computer program, and when the computer program is executed, the above-mentioned measurement correction method of SOC based on OCV-SOC estimation is implemented.

[0096] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0097] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0098] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0100] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented by a combination of any one or more of the following techniques known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0101] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0102] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0103] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for measuring and correcting SOC based on OCV-SOC estimation, characterized in that: The following steps are involved: Extract measured data of hybrid power pulse characteristics HPPC of lithium-ion batteries; Based on the measured data, estimating the steady-state open circuit voltage OCV of the lithium-ion battery; Obtain the battery OCV-state of charge SOC measured curve at different temperatures; Establishing an OCV-SOC mapping relationship according to the battery OCV-SOC measured curve; Correct the SOC estimation value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship; Wherein, estimating the steady-state OCV of the lithium-ion battery includes: Obtaining a charging envelope and a discharging envelope according to the measured data; Obtaining the steady-state OCV according to an average value of the charging envelope and the discharging envelope; Alternatively, estimating the steady-state OCV of the lithium-ion battery comprises: Based on the measured data, an exponential curve is used to best fit the data within the preset gap where the discharge is stopped, to obtain a steady-state extrapolated value; Based on the steady-state extrapolated value, the steady-state OCV is determined as an average value of an upper limit of the OCV region during charge and a lower limit of the OCV region during discharge.

2. The method according to claim 1, characterized in that The establishing of an OCV-SOC mapping relationship according to the battery OCV-SOC measured curve includes: The OCV-SOC measured curve of the battery is fitted piecewise to obtain the OCV-SOC mapping relationship.

3. The method according to claim 1, characterized in that The establishing of an OCV-SOC mapping relationship according to the battery OCV-SOC measured curve includes: Based on the measured OCV-SOC curve of the battery, the OCV-SOC mapping relationship is acquired using a preset gradient boosting tree.

4. A measurement and correction device for SOC based on OCV-SOC estimation, characterized in that: include: An extraction module, used to extract the measured data of HPPC of lithium-ion batteries; An estimation module, configured to estimate the steady-state OCV of the lithium-ion battery based on the measured data; Acquisition module, used to obtain the measured OCV-SOC curve of the battery at different temperatures; A generating module, used to establish an OCV-SOC mapping relationship according to the battery OCV-SOC measured curve; A correction module, configured to correct the SOC estimation value under the mapping according to the steady-state OCV and the OCV-SOC mapping relationship; Wherein, the estimation module comprises: A first generating unit, configured to obtain a charging envelope and a discharging envelope according to the measured data; A second generating unit, configured to obtain the steady-state OCV according to an average value of the charging envelope and the discharging envelope; Alternatively, the estimation module comprises: A fitting unit, used for performing optimal fitting of the data in the preset gap where the discharge is stopped using an exponential curve based on the measured data to obtain a steady-state extrapolated value; A determination unit is used to determine the steady-state OCV based on the steady-state extrapolated value by taking an average value of an upper limit of the OCV region during charging and a lower limit of the OCV region during discharging.

5. The device according to claim 4, characterized in that The generating module comprises: The third generating unit is used to perform piecewise fitting on the battery OCV-SOC measured curve to obtain the OCV-SOC mapping relationship.

6. The device according to claim 4, characterized in that The generating module comprises: An acquisition unit is used to acquire the OCV-SOC mapping relationship based on the battery OCV-SOC measured curve by using a preset gradient boosting tree.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for measuring and correcting SOC based on OCV-SOC estimation as described in any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the SOC measurement correction method based on OCV-SOC estimation as described in any one of claims 1 to 3.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it is used to implement the SOC measurement correction method based on OCV-SOC estimation as described in any one of claims 1 to 3.

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